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Hello.

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And this will you.

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We will create a very simple single Perceptron model to classify flower species depending on their Bitel

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length and pattern.

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We will be using a Skillern to create this single Perceptron model.

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In the latter part of this course, we will use Get US and 10 SAP flow to create multi-level Perceptron

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models, as Skillern is a very popular machine learning liablity for Python.

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It is the go to library to create regression classification decision trees or as we are models, we

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have separate lectures on all this machine learning models.

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So if you are interested in learning any one of these, you can go ahead and check out what courses

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on regression classification be Sentry's and SVM.

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So let's start first.

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We will import numbers and find us.

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Now, if you have installed Anakonda.

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There is no need to install a skillern separately.

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You just have to import Eskinder.

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But in case you are facing any error in importing a Skillern, you can install it using BEP or Condah

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installed.

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So just run this.

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Come on, Pip, install a Skillern in your command prompt.

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You can open your command prompt by pressing windows odd and write CMB and that one come on and hit

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enter there in the command prompt.

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You can write this code and execute it to install Escalon.

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What else you can directly lead on this code?

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And despite the notebook and this will also install Escalon for you.

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So if you have installed a Skillern.

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First, we will load iris data.

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There are various CSP files out there.

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You can also import those see as we file to load this data.

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But as Skillern have some predefined datasets and we will load our ideas data from there.

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So just right from Eskil under datasets.

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Import load.

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Underscore itis.

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And then we are saving the world IRI's data into this variable iris.

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So just run this.

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Now, let's take a look at the data, you can see there are four columns.

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To call, said Sepur Land Sepúlveda by the land and Paton's with, as I have said earlier this year,

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data of different types of flowers.

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There are three different types of flowers, Sentosa.

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What's the color virginica?

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And for each of this flower, we have their Sepur length Sepúlveda.

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But the length and petals.

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So here we have this four variables and that category is stored in Iris Dort Target.

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So in our example, we want to create a Perceptron model which would identify whether the flower what

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is Sentosa or not using by Dalvik and by the length as the independent variables.

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If you want, you can take all the four variables.

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But for example, we are just taking these two variables to predict where the flower is Towser or not.

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So we want our independent variable to be this petal length and petal look.

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We will store this information in another variable which we are calling X.

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We are defining X as itis DOT data.

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And here we just want the third and fourth column.

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That's why we have returned to comment three.

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If you remember, the indexing starts with zero.

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So the last two columns are two and three.

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Just run this.

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So over X variable is now ready.

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Now let's just look at the target variable.

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Here you can see we have different categories, zero one and two zero percent for Sentosa.

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One sentence for what Ziggler and two stands for Virginica.

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Now, if you have some machine learning knowledge, you may know that to create classification more

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than what y variable should be in the form of zero and one.

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So ideally we want one and all these records where the flower is set, Oza and zero and all this records

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where the flower is vertical or virginica.

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So let's just convert this target, we able using some basic operations first.

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Let's convert this target variable in the form of true and false.

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We want to where the flowery sentosa.

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That is the value of target is zero.

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And we want false where the flower is Virginica or Cicala.

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That is the numerical value is one or two.

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So let's just blend this.

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Come on.

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We are just checking whether the target is equal to zero or not.

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If the target is zero, we will get group.

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And if the target is not equal to zero, we will get false.

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Let's run this.

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Now let's look at our very well.

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You can see we have converted zeros, two crew and one or two boot false.

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Now, in the next step, we want to convert this windfalls falls to one zero.

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We want one in place of all.

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And we want zero in place of false.

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So we will just use as type method and we will convert it to.

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And so just on and on look at our way variable.

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So as you can see now, our way variable is an array with one and zero.

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One means that Dorsa and zero means not sentosa.

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Now let's look at our X variable before creating a Perceptron model.

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You can see our X variable is a two dimensional array with by the length and patella with.

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No one would extend Liberty Bell, said Brady.

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We just have to create our Perceptron model and dream that model using this X and Y variables.

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We can create a single Perceptron more than using a Skillern, but to create MLP on my level, but it's

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separate more than we have to use Cadeaux Intensive.

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We will be looking at l.P in the later part of this Goetze.

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But now we will create this sub growth model using Escalon only.

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Now first we need to import Perceptron from Eskil and Autoline anymore.

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Then let's just import it.

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And you can also look at the documentation using this link.

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This Dubai term notebook is also shared in the resources section of this video.

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So you can download it and learn it if you want to practice.

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So this is the official document of Perceptron in Skillern.

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You can look at all the parameters which we can give, the first one is the penalty.

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If you remember, in linear regression, there are regularization terms, L1, a clue also known as

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LASO and Rich.

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You can also give us a thought that is the variable we use for regularization.

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Then there are different other hyper parameters.

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We will just stick to basic before hyper parameters.

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But you can look at it if you want.

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These are just some very basic parameters that we get with most of the machine learning algorithms.

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Now, as with any other on machine learning, more than we first have to create an object of that algorithm,

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then we have to fit over X and Y variables and to that object and then we can use that object to predict

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the future values of way.

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So first, let's create an object that is Perceptron classifier.

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We are naming Autobody Willis, but underscored see L.F. and then we are using Perceptron that we have

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just imported and we are giving only one hyper parameter.

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That is a random mistake to equate to 42.

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This is basically to reproduce the same result whenever we run this model.

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So if you give random set, you will always going to get the same result.

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Giving this hyper parameter is not mandatory.

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You can skip that also if you want.

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And in the next lane, we are putting our X and Y variables into this object.

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So let's find this.

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So what model is screened?

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You can get the values of before you type in.

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But I might add here if you want.

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Now, let's clear the value using overclassify, our object.

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We can just use DOT, predict my.

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So this is the word object and we are using DOT predict and we are giving our independent and variable,

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which is X as an input.

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Let's do that.

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You can see this are the predicted values.

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We can compare this predicted values with the actual values.

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There is no need to manually do that.

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That isn't another function.

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That is accuracy scored available in Escalon, which will give us the accuracy of what prediction accuracy

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score vary between zero and one zero means zero percent accuracy, which means all the predictions are

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wrong.

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And one means hundred percent accuracy, which meant all the predictions are right.

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So let's just first import accuracy score from a scalar, not my cricks.

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And then we are going to use accuracy score.

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And here there are two arguments.

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First, we have to provide the actual values.

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And in the next argument, we have put away the predicted values.

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So what actual values are restored in just five variable?

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And the predicted values?

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We have restored in VI underscore pride.

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So let's get the accuracy score of forward predictions.

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So the accuracy here is one that is hundred percent accuracy or what?

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Perceptron was able to identify the speci of the float with 100 percent accuracy.

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Now, this is a very simple model.

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Usually we don't use Perceptron for any regression or classification tasks.

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We usually go for machine learning techniques where the data is not falling.

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And in context, Petten and we use really well for an LP.

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We're.

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The details following a very complex pattern.

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Usually you will never find yourself using Perceptron and business settings.

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But this is just an introduction.

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And we want you to give just some basic idea about running Perceptron models using Escolar.

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Now, after training your mother, you will get these two attributes.

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That is the coefficient.

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And then Sept.

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Basically over Perceptron is dividing this space using linear regression line.

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So this is the coefficient.

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And this is the intercept of that line.

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So your equation of that line will be four minus one point four times by the length minus two point

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two times.

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But Elphick.

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So if you want to create that line, you can use coefficient and intercept values to do that, and you

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can also see the impact of our different variables on the Y variable.

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So this coefficient is giving you the impact of each of those variables.

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This is the impact of the land and this the impact of pattern.

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That's all for this lecture.

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Next, we will look at tensor flow and get us to create our MLP model.

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Thank you.